def setUp(self):
self.dir_ = data_dir + "pipelines_test_resources/"
#use as a conversion tool, creates the files we want
bcs.main([
"build_core_space.py", "-l", self.dir_ + "log1.txt", "-i",
self.dir_ + "N_mat", "-o", self.dir_, "--input_format", "dm"
])
bcs.main([
"build_core_space.py", "-l", self.dir_ + "log1.txt", "-i",
self.dir_ + "AN_mat", "-o", self.dir_, "--input_format", "dm"
])
bcs.main([
"build_core_space.py", "-l", self.dir_ + "log1.txt", "-i",
self.dir_ + "A_mat", "-o", self.dir_, "--input_format", "dm"
])
tc.main([
"train_composition.py", "-l", self.dir_ + "log1.txt", "-i",
self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m",
"lexical_func", "-p", self.dir_ + "CORE_SS.AN_mat.pkl", "-a",
self.dir_ + "CORE_SS.N_mat.pkl", "-r", "lstsq", "--intercept",
"False", "--export_params", "True"
])
def test_simple_lstsq_inter(self):
tc.main(["train_composition.py",
"-l", self.dir_ + "log1.txt",
"-i", self.dir_ + "an_train_data.txt",
"-o", self.dir_,
"-m", "lexical_func",
"-p", self.dir_ + "CORE_SS.AN_mat.pkl",
"-a", self.dir_ + "CORE_SS.N_mat.pkl",
"-r", "lstsq",
"--intercept", "True",
"--export_params", "True",
])
trained = io_utils.load(self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl")
new_space = trained.function_space
np.testing.assert_array_almost_equal(new_space.cooccurrence_matrix.mat,
np.mat([[0.66666667,0.33333333,
-0.33333333,0.33333333,
0.66666667,0.33333333]]),
7)
self.assertTupleEqual(new_space.element_shape, (2,3))
self.assertListEqual(new_space.id2row, ["big"])
self.assertListEqual(new_space.id2column, [])
a_space = Space.build(data=self.dir_ + "TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.params.dm",
format="dm")
self._test_equal_spaces_dense(a_space, new_space)
def test_simple_lstsq_no_inter(self):
tc.main([
"train_composition.py", "-l", self.dir_ + "log1.txt", "-i",
self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m",
"lexical_func", "-p", self.dir_ + "CORE_SS.AN_mat.pkl", "-a",
self.dir_ + "CORE_SS.N_mat.pkl", "-r", "lstsq", "--intercept",
"False", "--export_params", "True"
])
trained = io_utils.load(
self.dir_ +
"TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl")
new_space = trained.function_space
np.testing.assert_array_almost_equal(new_space.cooccurrence_matrix.mat,
np.mat([1, 0, 0, 1]), 10)
self.assertTupleEqual(new_space.element_shape, (2, 2))
self.assertListEqual(new_space.id2row, ["big"])
self.assertListEqual(new_space.id2column, [])
a_space = Space.build(
data=self.dir_ +
"TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.params.dm",
format="dm")
self._test_equal_spaces_dense(a_space, new_space)
tc.main([
"train_composition.py", "-l", self.dir_ + "log1.txt", "-i",
self.dir_ + "an_train_data.txt", "-o", self.dir_, "-m",
"lexical_func", "-p", self.dir_ + "CORE_SS.AN_mat.pkl", "-a",
self.dir_ + "CORE_SS.N_mat.pkl", "-r", "ridge", "--lambda", "0",
"--crossvalidation", "False", "--intercept", "False",
"--export_params", "True"
])
trained = io_utils.load(
self.dir_ +
"TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.pkl")
new_space2 = trained.function_space
np.testing.assert_array_almost_equal(
new_space2.cooccurrence_matrix.mat, np.mat([1, 0, 0, 1]), 10)
self.assertTupleEqual(new_space2.element_shape, (2, 2))
self.assertListEqual(new_space2.id2row, ["big"])
self.assertListEqual(new_space2.id2column, [])
a_space = Space.build(
data=self.dir_ +
"TRAINED_COMP_MODEL.lexical_func.an_train_data.txt.params.dm",
format="dm")
self._test_equal_spaces_dense(a_space, new_space2)
def setUp(self):
self.dir_ = data_dir + "pipelines_test_resources/"
#use as a conversion tool, creates the files we want
bcs.main(["build_core_space.py",
"-l", self.dir_ + "log1.txt",
"-i", self.dir_ + "N_mat",
"-o", self.dir_,
"--input_format", "dm"
])
bcs.main(["build_core_space.py",
"-l", self.dir_ + "log1.txt",
"-i", self.dir_ + "AN_mat",
"-o", self.dir_,
"--input_format", "dm"
])
bcs.main(["build_core_space.py",
"-l", self.dir_ + "log1.txt",
"-i", self.dir_ + "A_mat",
"-o", self.dir_,
"--input_format", "dm"
])
tc.main(["train_composition.py",
"-l", self.dir_ + "log1.txt",
"-i", self.dir_ + "an_train_data.txt",
"-o", self.dir_,
"-m", "lexical_func",
"-p", self.dir_ + "CORE_SS.AN_mat.pkl",
"-a", self.dir_ + "CORE_SS.N_mat.pkl",
"-r", "lstsq",
"--intercept", "False",
"--export_params", "True"
])
